To avoid these limitations, this work adopts an off-the-shelf transductive-VOS network [1]} for video object segmentation, which is trained on the Davis 2017 [2]} and Youtube-VOS [3]} datasets. The network uses dense long-term similarity dependencies between current and past feature embeddings to propagate the previous object mask to the latest frame. The object mask needed by BundleTrack is simply binary, i.e., \(M_{\tau }=\lbrace 0,1\rbrace ^{H \times W}, \tau \in \lbrace 0,1,...,t\rbrace \)  and distinguishes the object region from the background. The only requirement is an initial mask \(M_{0}\)  of interest. Neither the transductive-VOS network nor the following steps of BundleTrack require \(M_{0}\)  to come from semantic/instance segmentation. Therefore, it can also be obtained in alternative ways depending on the application, e.g., low-level image segmentation [4]}, [5]}, point cloud segmentation/clustering [6]}, [7]}, or plane fitting and removal [6]}, etc.
